<h1>Probabilistic Machine Learning: An Introduction</h1>
by <a href="https://www.cs.ubc.ca/~murphyk/">Kevin Patrick Murphy</a>.
<br>
MIT Press, 2021.


<h2>Key links</h2>

<ul>

  <li> <a href="pml1/pml1-toc-short-2020-12-26.pdf">Short table of
      contents</a>

  <li> <a href="pml1/pml1-toc-long-2020-12-26.pdf">Long table of
      contents</a>

  <li> <a href="pml1/pml1-preface-2020-12-26.pdf">Preface</a>

  <li> <a href="
    https://github.com/probml/pml-book/releases/latest/download/pml1.pdf
    ">Draft pdf file</a>,  CC-BY-NC-ND license. (Please cite the official reference below.)

	  
    <li> <a href="https://github.com/probml/pyprobml">Python code</a>
      
  <li> <a href="https://github.com/probml/pml-book/issues">Issue tracker</a>. Use this to report problems with the book and/or code. Be sure to specify which 
    release (date stamp) of the book you are using. Please specify pdf and print page number (which sometimes differ). For reporting small typos, please collect
    a batch of errors into a doc, and create a single issue (or add to an existing open issue list).
</ul>

If you use this book, please be sure to cite
<pre><code>
 @book{pml1Book,
 author = "Kevin P. Murphy",
 title = "Probabilistic Machine Learning: An introduction",
 publisher = "MIT Press",
 year = 2021,
 url = "probml.ai"
}
</code></pre>

  
  
<h2>Table of contents</h2>

<br>
    <img SRC="pml1/pml1-toc-short-2col-2020-12-27.png"
     alt="TOC 2020-12-26"
     style="height:400;">

  
<p>
<h2><a id="endorsements"></a>Endorsements</h2>

<ul>

    <li> 
  <img src="https://img.shields.io/github/downloads/probml/pml-book/total"
       alt="download stats shield">
  since first release in 2020-12-28.

  <p>
    <li>
      "Kevin Murphy’s book on machine learning is a superbly written,
      comprehensive treatment of the field, built on a foundation of probability theory.
      It is rigorous yet readily accessible, and
      is a must-have for anyone interested in gaining a deep understanding of machine learning."
	    -- <a href="https://www.microsoft.com/en-us/research/people/cmbishop/">Chris Bishop</a>,
	Microsoft Research.

<p>
	<li>
		"This is a remarkable book covering the conceptual,
		theoretical and computational foundations of probabilistic machine learning, 
		starting with the basics and moving seamlessly to the leading edge of this field.  
		The pedagogical structure of the book is extremely useful for teaching. One of my favorite parts is
		that most of the figures  of the book have a link to the associated 
		(python, JAX, tensorflow) code that is used to generate them,
		often with comparisons between the different computational ways of solving the problems."
		-- <a href="https://www.seas.harvard.edu/brenner/Home.html">Michael Brenner</a>, Harvard.
		
	  <p>
		  <li>
	  "This book could be titled 'What every ML PhD student should know'. 
	  If you master the material in this book, you will have an outstanding foundation for successful research in machine learning.”
			  -- <a href="http://web.engr.oregonstate.edu/~tgd/">Tom Dietterich</a>, U. Oregon

    <p>
    <li> "There are many books on machine learning out there, but none gives
    such a well-rounded, up-to-date, and comprehensive view of the
    field as this one. We use this book as reference reading for our
    students taking the advanced machine learning course at Oxford to
    introduce them to fundamental as well as current topics in the
    field. I'm amazed at the amount of work that went into this
    book---which will surely be used by many to train the next
      generation of machine learning experts."
      -- <a href="http://www.cs.ox.ac.uk/people/yarin.gal/website/">Yarin    Gal</a>, U. Oxford

	    
	  <p>
	    <li>
	      "This is a terrific resource for machine learning students and researchers.
		    If you want to understand the foundations of modern machine learning then this is the book to read. 
		    The text is particularly strong at marrying classical ideas from statistics and probability with more modern concepts such as deep learning."
	      -- <a href="https://www.ics.uci.edu/~smyth/">Padhraic Smyth</a>, UC Irvine
		    
		          <p>
  <li> "My favorite machine learning book just received a face-lift!
    'Probabilistic Machine Learning: An Introduction' is the most
    comprehensive and accessible book on modern machine learning by a
    large margin.  
    It now also covers the latest developments in deep learning and
    causal discovery. With this upgrade it will remain the reference
    book for our field that every respected researcher needs to have
    on their desk."  -- <a href="https://staff.fnwi.uva.nl/m.welling/">Max Welling</a>,
    U. Amsterdam 
	 
		    
      <p>
	<li>
	  "Prof Murphy's 2012 book was a triumph, covering both basic material
and also the state-of-the-art. The new 'Probabilistic Machine
Learning: An Introduction' is similarly excellent, and includes new
material, especially on deep learning and recent developments.  It
will become an essential reference for students and researchers in
	  probabilistic machine learning."
	  -- <a href="https://homepages.inf.ed.ac.uk/ckiw/">Chris Williams</a>, U. Edinburgh

	  


    </ul>


  

  <p>
<h2><a id="ack"></a>Acknowledgements</h2>

I would like to thank the following people for helping with this book.

<ul>

<li> People who helped write some sections:
	Mathieu Blondel,
  Krzysztof Choromanski,
  Justin Gilmer,
  Zico Kolter,
  Frederick Kunster,
  Lihong Li,
  Si Yi Meng,
  Aaron Mishkin,
  Byran Perozzi,
  Colin Raffel,
  Mark Schmidt,
  Sharan Vaswani,
  Andrew Wilson.
  
      <li> Proof reader: John Fearns.

      <li> People who have provided feedback on parts of the book:
Sebastien Bratieres,
	Kai Brodersen,
	Peter Cerno,
Daniel Galvez,
Abhishek Kumar,
Max Lepikhin,
Aaron Michelony,
	      Horst Stühler,
Hal Varian,
	      Clem Wang.

      <li> People who have helped with the code:
	Andrew Carr,
	Aurelien Geron,
	      Gerardo Durán Martín,
	Osvaldo Martin,
	Duane Rich,
	Mahmoud Soliman,
	Theodore Vasiloudis,
	Oscar Wahltinez.

      <li> People who have helped with the figures:
	Sandeep Choudhary, and others who are credited in the figure captions.
    </ul>
    

